Hey, I'm Xinyan DAI!
- 🔭 I was awarded a Ph.D. at CUHK, in machine learning and similarity search
string embed for fast edit distance computation, codes for [Convolutional Embedding for Edit Distance (SIGIR 20)].
License: MIT License
Hi,
Thanks for the great work and the ingenious paper!
I trained a model based on the line that is given on the project's page, specifically:
python main.py --dataset word --nt 1000 --nq 1000 --epochs 20 --save-split --recall --save-model
Now I have the trained model (I know it is not the version that is presented in the paper), and a list of two words, how can I use the model in order to predict their edit distance?
I got lost going through the preprocessing stage.
Thanks again, and sorry for the noob question :)
I believe I found other datasets that your paper reference (eg Enron, Uniref, gen50ks) here: https://github.com/kedayuge/Embedjoin/blob/master/README.md
I was wondering if you can provide the command line for the recommended arguments to run on these other datasets, as well as the expected run time for them? I noticed that a table of edit distances needs to be made, which can run in quadratic time with dataset size.
Lines 71 to 72 in 61e0347
It seems to be this code is making x
to mix values from different batch instances. Instead, I think it should be x = x.view(x.size(0), self.mtc_input, -1)
Hi, thanks for sharing the code from the paper! I think I found a bug, looks like a git merge issue:
Lines 412 to 425 in 8a8a2ea
Should be only one of the self.Ls
, right?
And it seems the current master version is not using the mse_loss
, since it's all zero.
就是retrieve topN的里面更有可能包含正确答案?
@xinyandai 多谢多谢多谢!
用编辑距离得到的数据作为训练数据训练一个模型,然后recall-item会提升?
正负例是如何构造的?比BM25效果怎样?
@xinyandai 多谢多谢多谢!
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